SYSTEMS AND METHODS FOR ADAPTIVE CONFORMAL PREDICTION

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SYSTEMS AND METHODS FOR ADAPTIVE CONFORMAL PREDICTION - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240249145 titled 'SYSTEMS AND METHODS FOR ADAPTIVE CONFORMAL PREDICTION

The abstract describes a patent application for a Strongly Adaptive Online Conformal Prediction (SAOCP) framework that utilizes multiple experts to predict respective prediction radii, each operating within its own active interval. The aggregated prediction radius is computed as a weighted sum of the predicted radii, with each expert's contribution weighted by the probability of their activity at the time step. The experts are operated using a Scale-Free Online Gradient Descent (SF-OGD) method to update the predicted radius, and a base conformal predictor generates a prediction set using the aggregated radius.

Multiple experts manage prediction radii within their active intervals
Aggregated prediction radius is a weighted sum of individual predictions
Experts use SF-OGD method to update predicted radii
Base conformal predictor generates prediction set using aggregated radius

Potential Applications: - Financial forecasting - Risk management - Weather prediction - Healthcare diagnostics

Problems Solved: - Improved accuracy of prediction intervals - Efficient management of multiple experts - Adaptive online prediction framework

Benefits: - Enhanced prediction accuracy - Real-time adaptive predictions - Increased reliability in forecasting

Commercial Applications: Title: Adaptive Online Prediction Framework for Financial Forecasting This technology could be used in financial institutions for more accurate risk assessment and investment decision-making. It could also be applied in weather forecasting services to provide more precise predictions for various industries.

Questions about the SAOCP Framework: 1. How does the SAOCP framework improve prediction accuracy compared to traditional methods? 2. What are the potential limitations of using multiple experts in the prediction process?

Frequently Updated Research: Stay updated on advancements in online prediction frameworks and machine learning algorithms to enhance the SAOCP framework's performance and applicability in various industries.

Original Abstract Submitted

embodiments described herein provide a strongly adaptive online conformal prediction (saocp) framework that manages multiple experts each for predicting a respective prediction radius, while each expert only operates on its own active interval. an aggregated prediction radius may be computed as a weighted sum of the predicted radii, each weighted by the respective probability that the respective expert is active at the time step. specifically, each expert may be operated with a scale-free ogd (sf-ogd) method to update the generated predicted radius. a base conformal predictor may then generate a prediction set using the aggregated radius at the time step.